Model interpretability beyond selective rationalization:  There are other popular interpretability frameworks besides selective rationalization. Module networks [1]}, [2]}, [3]} compose appropriate neural modules following a logical program to complete the task. Their applicability is relatively limited, due to the requirement of pre-defined modules and programs. Evaluating feature importance with gradient information [4]}, [5]}, [6]}, [7]} is another popular method. Though [4]} discusses several advantages of gradient-based methods over rationalization, they are post-hoc and cannot impose structural constraints on the explanation. Other lines of work that provide post-hoc explanations include local perturbations [9]}, [10]}; locally fitting interpretable models [11]}, [12]}; and generating explanations in the form of edits to inputs that change model prediction to the contrast case [13]}.
